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Title: Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development

Abstract

Small commercial buildings (those with less than approximately 1000 m 2 of total floor area) often do not have access to cost-effective automated fault detection and diagnosis (AFDD) tools for maintaining efficient building operations. AFDD tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, such algorithms require access to high-quality training data that is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus® and OpenStudio® to generate a cost-effective training data set for developing AFDD algorithms. Part I (this paper) presents a library of fault models, including detailed descriptions of each fault model structure and their implementation with EnergyPlus. This paper also discusses a case study of training data set generation, representing an actual building.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Purdue Univ., West Lafayette, IN (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1580571
Report Number(s):
NREL/JA-5500-75713
Journal ID: ISSN 2075-5309
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Buildings
Additional Journal Information:
Journal Volume: 9; Journal Issue: 11; Journal ID: ISSN 2075-5309
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; automated fault detection and diagnosis; data-driven AFDD; fault model; building energy modeling; EnergyPlus; OpenStudio

Citation Formats

Kim, Janghyun, Frank, Stephen M., Braun, James E., and Goldwasser, David. Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development. United States: N. p., 2019. Web. doi:10.3390/buildings9110233.
Kim, Janghyun, Frank, Stephen M., Braun, James E., & Goldwasser, David. Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development. United States. doi:10.3390/buildings9110233.
Kim, Janghyun, Frank, Stephen M., Braun, James E., and Goldwasser, David. Thu . "Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development". United States. doi:10.3390/buildings9110233. https://www.osti.gov/servlets/purl/1580571.
@article{osti_1580571,
title = {Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development},
author = {Kim, Janghyun and Frank, Stephen M. and Braun, James E. and Goldwasser, David},
abstractNote = {Small commercial buildings (those with less than approximately 1000 m2 of total floor area) often do not have access to cost-effective automated fault detection and diagnosis (AFDD) tools for maintaining efficient building operations. AFDD tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, such algorithms require access to high-quality training data that is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus® and OpenStudio® to generate a cost-effective training data set for developing AFDD algorithms. Part I (this paper) presents a library of fault models, including detailed descriptions of each fault model structure and their implementation with EnergyPlus. This paper also discusses a case study of training data set generation, representing an actual building.},
doi = {10.3390/buildings9110233},
journal = {Buildings},
number = 11,
volume = 9,
place = {United States},
year = {2019},
month = {11}
}

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Works referenced in this record:

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Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part II
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